John S. Tamaresis, PhD, MS

Biostatistician, Biomedical Data Science-Administration

Bio

Bio

Dr. Tamaresis joined the Stanford University School of Medicine in Summer 2012. He earned the Ph.D. in Applied Mathematics from the University of California, Davis and received the M.S. in Statistics from the California State University, East Bay. He has conducted research in computational biology as a postdoctoral scholar at the University of California, Merced and as a biostatistician at the University of California, San Francisco.

As a statistician, Dr. Tamaresis has developed and validated a highly accurate statistical biomarker classifier for gynecologic disease by applying multivariate techniques to a large genomic data set. His statistical consultations have produced data analyses for published research studies and analysis plans for novel research proposals in grant applications. As an applied mathematician, Dr. Tamaresis has created computational biology models and devised numerical methods for their solution. He devised a probabilistic model to study how the number of binding sites on a novel therapeutic molecule affected contact time with cancer cells to advise medical researchers about its design. For his doctoral dissertation, he created and analyzed the first mathematical system model for a mechanosensory network in vascular endothelial cells to investigate the initial stage of atherosclerotic disease.

Abstract

Central venous access devices (CVADs) are used in the care of paediatric haemophilic patients with difficult peripheral access, but their use is limited by complications such as infection. We previously published our experience with monthly recombinant tissue plasminogen activator (r-tPA) administration to CVADs of haemophilic patients as an intervention for infection prophylaxis, which suggested a 10-fold decrease in infection rate compared to published rates without r-tPA.This study was conducted to assess the CVAD infection rate in an expanded haemophilia cohort receiving r-tPA over an extended period.A retrospective review was performed on patients with haemophilia who received monthly r-tPA to CVADs, with data collected from January 1, 2008 to December 31, 2012. The data were merged with the previously reported data set (collected from June 1, 1998 to December 31, 2007).Over the entire observation period, there were 46 350 CVAD days among 32 patients [26 severe factor VIII (FVIII) deficiency, six severe FIX deficiency]. Eight patients received immune tolerance therapy for inhibitors and 24 patients received prophylactic factor administration. No patients were HIV positive. Three infections were observed, with an overall infection rate of 0.06 infections per 1000 CVAD days.A low CVAD infection rate, similar to that observed in our previous study (0.04 per 1000 CVAD days), was observed in this expanded haemophilia cohort treated with prophylactic r-tPA, supporting the use of monthly r-tPA as CVAD infection prophylaxis in haemophilia patients.

Abstract

Children born preterm (at ?32wks) are at risk of developing deficits in reading ability. This meta-analysis aims to determine whether or not school-aged preterm children perform worse than those born at term in single-word reading (decoding) and reading comprehension.Electronic databases were searched for studies published between 2000 and 2013, which assessed decoding or reading comprehension performance in English-speaking preterm and term-born children aged between 6 years and 13 years, and born after 1990. Standardized mean differences in decoding and reading comprehension scores were calculated.Nine studies were suitable for analysis of decoding, and five for analysis of reading comprehension. Random-effects meta-analyses showed that children born preterm had significantly lower scores (reported as Cohen's d values [d] with 95% confidence intervals [CIs]) than those born at term for decoding (d=-0.42, 95% CI -0.57 to -0.27, p<0.001) and reading comprehension (d=-0.57, 95% CI -0.68 to -0.46, p<0.001). Meta-regressions showed that lower gestational age was associated with larger differences in decoding (Q[1]=5.92, p=0.02) and reading comprehension (Q[1]=4.69, p=0.03) between preterm and term groups. Differences between groups increased with age for reading comprehension (Q[1]=5.10, p=0.02) and, although not significant, there was also a trend for increased group differences for decoding (Q[1]=3.44, p=0.06).Preterm children perform worse than peers born at term on decoding and reading comprehension. These findings suggest that preterm children should receive more ongoing monitoring for reading difficulties throughout their education.

Abstract

Endometriosis (E), an estrogen-dependent, progesterone-resistant, inflammatory disorder, affects 10% of reproductive-age women. It is diagnosed and staged at surgery, resulting in an 11-year latency from symptom onset to diagnosis, underscoring the need for less invasive, less expensive approaches. Because the uterine lining (endometrium) in women with E has altered molecular profiles, we tested whether molecular classification of this tissue can distinguish and stage disease. We developed classifiers using genomic data from n = 148 archived endometrial samples from women with E or without E (normal controls or with other common uterine/pelvic pathologies) across the menstrual cycle and evaluated their performance on independent sample sets. Classifiers were trained separately on samples in specific hormonal milieu, using margin tree classification, and accuracies were scored on independent validation samples. Classification of samples from women with E or no E involved 2 binary decisions, each based on expression of specific genes. These first distinguished presence or absence of uterine/pelvic pathology and then no E from E, with the latter further classified according to severity (minimal/mild or moderate/severe). Best performing classifiers identified E with 90%-100% accuracy, were cycle phase-specific or independent, and used relatively few genes to determine disease and severity. Differential gene expression and pathway analyses revealed immune activation, altered steroid and thyroid hormone signaling/metabolism, and growth factor signaling in endometrium of women with E. Similar findings were observed with other disorders vs controls. Thus, classifier analysis of genomic data from endometrium can detect and stage pelvic E with high accuracy, dependent or independent of hormonal milieu. We propose that limited classifier candidate genes are of high value in developing diagnostics and identifying therapeutic targets. Discovery of endometrial molecular differences in the presence of E and other uterine/pelvic pathologies raises the broader biological question of their impact on the steroid hormone response and normal functions of this tissue.

Abstract

Our ultimate goal is to detect the entire human microbiome, in health and in disease, in a single reaction tube, and employing only commercially available reagents. To that end, we adapted molecular inversion probes to detect bacteria using solely a massively multiplex molecular technology. This molecular probe technology does not require growth of the bacteria in culture. Rather, the molecular probe technology requires only a sequence of forty sequential bases unique to the genome of the bacterium of interest. In this communication, we report the first results of employing our molecular probes to detect bacteria in clinical samples.While the assay on Affymetrix GenFlex Tag16K arrays allows the multiplexing of the detection of the bacteria in each clinical sample, one Affymetrix GenFlex Tag16K array must be used for each clinical sample. To multiplex the clinical samples, we introduce a second, independent assay for the molecular probes employing Sequencing by Oligonucleotide Ligation and Detection. By adding one unique oligonucleotide barcode for each clinical sample, we combine the samples after processing, but before sequencing, and sequence them together.Overall, we have employed 192 molecular probes representing 40 bacteria to detect the bacteria in twenty-one vaginal swabs as assessed by the Affymetrix GenFlex Tag16K assay and fourteen of those by the Sequencing by Oligonucleotide Ligation and Detection assay. The correlations among the assays were excellent.

Abstract

Arterial endothelial cell (EC) responsiveness to flow is essential for normal vascular function and plays a role in the development of atherosclerosis. EC flow responses may involve sensing of the mechanical stimulus at the cell surface with subsequent transmission via cytoskeleton to intracellular transduction sites. We had previously modeled flow-induced deformation of EC-surface flow sensors represented as viscoelastic materials with standard linear solid behavior (Kelvin bodies). In the present article, we extend the analysis to arbitrary networks of viscoelastic structures connected in series and/or parallel. Application of the model to a system of two Kelvin bodies in parallel reveals that flow induces an instantaneous deformation followed by creeping to the asymptotic response. The force divides equally between the two bodies when they have identical viscoelastic properties. When one body is stiffer than the other, a larger fraction of the applied force is directed to the stiffer body. We have also probed the impact of steady and oscillatory flow on simple sensor-cytoskeleton-nucleus networks. The results demonstrated that, consistent with the experimentally observed temporal chronology of EC flow responses, the flow sensor attains its peak deformation faster than intracellular structures and the nucleus deforms more rapidly than cytoskeletal elements. The results have also revealed that a 1-Hz oscillatory flow induces significantly smaller deformations than steady flow. These results may provide insight into the mechanisms behind the experimental observations that a number of EC responses induced by steady flow are not induced by oscillatory flow.